| CPC G16H 50/30 (2018.01) [G16H 50/20 (2018.01); G16H 50/50 (2018.01)] | 18 Claims |

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1. A computer-based method of risk-based monitoring of a patient, the method comprising:
generating, by the computer, predicted probability density functions of internal state variables for a subsequent time step (tk+1), wherein the predicted probability density functions are calculated using posterior estimated probability density functions from a preceding time step (tk);
acquiring, by the computer at subsequent time step (tk+1), physiological data from a set of sensors connected with the patient;
generating a conditional likelihood kernel for the subsequent time step (tk+1), the conditional likelihood kernel comprising conditional probability density functions of the physiological data acquired at subsequent time step (tk+1) given the predicted probability density functions of internal state variables for subsequent time step (tk+1);
continuously estimating a risk that a particular bio-marker of the patient is abnormal, wherein the particular bio-marker comprises a hidden internal state variable, because it exceeds, by being either above or below, a corresponding pre-defined clinically significant value for that bio-marker, by:
generating, using Bayes theorem operating on (a) the conditional likelihood kernel and (b) predicted probability density functions of internal state variables for subsequent time step (tk+1), posterior probability density functions for the plurality of the internal state variables for the subsequent time step (tk+1); and
generating, for the particular bio-marker and based on the posterior probability density functions for the subsequent time step (tk+1), a probability that the particular bio-marker exceeds the corresponding pre-defined threshold for that particular bio-marker; and
generating, for display on a display device, a graphical depiction of the risk that the bio-marker is abnormal.
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